class_docstr: Base class for text detection module loss.
      
docstr: Generates loss targets from data samples. Returns a tuple of target
  tensors.
  
  Args:
      data_samples (list(TextDetDataSample)): Ground truth data samples.
  
  Returns:
      tuple: A tuple of target tensors.
docstr_tok: ["Generates","loss","targets","from","data","samples",".","Returns","a","tuple","of","target","tensors",".","Args",":","data_samples","(","list","(","TextDetDataSample",")",")",":","Ground","truth","data","samples",".","Returns",":","tuple",":","A","tuple","of","target","tensors","."]
code: def get_targets(self, data_samples: DetSampleList) -> Tuple:
      """Generates loss targets from data samples. Returns a tuple of target
      tensors.
  
      Args:
          data_samples (list(TextDetDataSample)): Ground truth data samples.
  
      Returns:
          tuple: A tuple of target tensors.
      """
      pass
  
file_path: mmocr/mmocr/models/textdet/module_losses/base.py
imports: from abc import ABCMeta, abstractmethod
  from typing import Dict, Sequence, Tuple, Union
  import torch
  from torch import nn
  from mmocr.registry import MODELS
  from mmocr.utils.typing_utils import DetSampleList
label_desc: Use image node_id 3 for calling the BaseTextDetModuleLoss obj's underlying member method code with example usage: obj.get_targets(data_samples) without return types